A neural network - could it work for you?

• Author: Smith, J. Clarke

A neural network--could it work for you?

The cream of the artificial-intelligence crop today is the neural network--systems that learn from their experiences. Read how you can use the sophisticated technology to improve your financial decision-making. The power of the computer is about to take another quantum leap, but this time the change won't be measured in more bytes for the buck or processing speed. It will be something entirely new: highly sophisticated computer systems with the ability to learn and to utilize accumulated experience to make specific, sound business decisions that rival, and perhaps surpass, those of a living, breathing human being.

Called neural networks, such artificial intelligence systems are built around concepts similar to the way the human brain's web of millions of neural connections (synapses) are believed to work together to identify patterns, learn, and reach conclusions.

Considered by some to be one of the most important technological advances of the last 10 years, neural network systems already are at work in the world of finance and elsewhere. They are particularly applicable to risk management and forecasting, where the ability to identify intricate patterns is crucial to making predictions. Nearly 100 companies reportedly are experimenting with neural network development.

Where do neural networks fit in?

In theory, a neural network can be put to work in any application where substantial amounts of data are used to predict outcome. Neural networks are being used today in applications ranging from analyzing engines to finding submarines.

For instance, one credit card company is using a neural network to identify the fraudulent use of plastic by spotting unusual purchasing activity. Neural networks are also being employed--with profitable results--in securities and options trading.

One insurance company is experimenting with a neural network to compare an individual agent's handwriting stored in a personal computer with the handwritten policy application forms the agent produces. Through pattern recognition, the computer can read each agent's handwriting, putting accurate data into memory without going through the potentially costly, error-prone method of having a clerk enter the data through a keyboard.

Another system, this one under development, will permit banks to use a neural network to recognize handwritten numerals on checks. Hundreds of thousands of checks will no longer have to be entered into the system by processing clerks.

Virtually all computers today operate through linear programming--applying complex sets of rules and thousands of yes-or-no, what-if answers to produce output. The fascination with and the practical significance in neural networks is that they make up their own rules. The more decisions they make, it appears, the better those decisions are.

But understanding how neural networks work is a job for Superman, a Ph.D. in mathematics, and perhaps both. Yet any CFO, and even CEO, risks falling behind in the competitive race without having at least a rudimentary knowledge of what neural networks are and just how they might be applied, so...